code: def env_maker(config):
      name = config.get("name", "MiniGrid-Empty-5x5-v0")
      framestack = config.get("framestack", 4)
      env = gym.make(name)
      # Make it impossible to reach goal by chance.
      env = gym.wrappers.TimeLimit(env, max_episode_steps=15)
      # Only use image portion of observation (discard goal and direction).
      env = minigrid.wrappers.ImgObsWrapper(env)
      env = OneHotWrapper(
          env,
          config.vector_index if hasattr(config, "vector_index") else 0,
          framestack=framestack,
      )
      return env
  
imports: from collections import deque
  import gymnasium
  import minigrid
  import numpy
  import sys
  import unittest
  import ray
  from ray import air, tune
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.ppo
  from ray.rllib.utils.test_utils import check_learning_achieved, framework_iterator
  from ray.rllib.utils.numpy import one_hot
  from ray.tune import register_env
label_desc: Use image node_id 1 for calling a global function with example usage: env_maker(config) and returns: env